Bibliographic record
Abstract
Network Screening is increasingly being used by road authorities as a tool to assist in their planning of investments in road improvements. Network screening can be defined as the process of measuring safety within a road network, with the objective of targeting road improvements to achieve the maximum cost-benefit. In British Columbia, the Insurance Corporation of British Columbia (ICBC) has been conducting network screening to prioritize locations for road safety improvements since the early 1990’s. Through its Road Improvement Program, ICBC partners with municipalities to identify and cost-share in upgrades to intersections and corridors. Results have shown significant savings in insurance claims and societal costs and returns on investment, through reduced injury and property damage collisions. In Alberta, the Alberta Motor Association has played a key role in assisting municipalities to identify improvements that can reduce insurance and societal costs associated with collisions, and several municipalities have subsequently set up their own network screening programs. Opus Hamilton has developed methodologies for and conducted numerous network screening exercises on behalf of several road agencies in both British Columbia and Alberta. The key methodologies of network screening are data review, nomination of locations and engineering correctability analysis. This paper will briefly describe these methodologies, provide examples of cost effective safety countermeasures, and report on how network screening has assisted in the decision-making for road improvements and ultimately in the responsible management of the road asset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".